The impact of privacy protection measures on the utility of crowdsourced cycling data

Raturi, V., Hong, J. , McArthur, D. and Livingston, M. (2021) The impact of privacy protection measures on the utility of crowdsourced cycling data. Journal of Transport Geography, 92, 103020. (doi: 10.1016/j.jtrangeo.2021.103020)

[img] Text
236203.pdf - Accepted Version
Restricted to Repository staff only until 25 September 2022.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

7MB

Abstract

The use of new forms of data in the transport research domain is rapidly gaining popularity. However, these data come with specific challenges and one of the major concerns is maintaining the privacy of data subjects. One widely used approach to anonymise the data is to apply binning. Recently, data from activity-tracking applications like Strava has been utilised to study and analyse active travel. Due to privacy concerns, Strava has started providing data in a discretised format from July 2018. In this study, we aim to analyse the impact of the binning criteria on the utility of the crowdsourced data by using Strava data from 2013 to 2016 for the city of Glasgow. We applied the Strava binning criteria on the original dataset at three different temporal aggregations (i.e., Hourly, Daily and Monthly) and conducted different analyses to examine its impacts. First, we compared manual cycling counts with original and binned cycling counts from Strava data. Second, net-errors were calculated by comparing original and binned cycling counts from Strava data. Third, we estimated spatial autocorrelation statistics based on original and binned Strava counts and investigated the extent to which research outcomes change because of the binning approach. Our results confirmed significant amount of information loss. Worryingly, we also show that conclusions reached by previous studies could have been reversed if the new specification of the data had been used. We outline here what precautions researchers and planners should take when working with the binned data.

Item Type:Articles
Additional Information:The authors would like to acknowledge support from the Economic and Social Research Council funded Urban Big Data Centre at the University of Glasgow (Grant ES/L011921/1, ES/S007105/1).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hong, Dr Jinhyun and Livingston, Dr Mark and Mcarthur, Dr David and Raturi, Dr Varun
Creator Roles:
Raturi, V.Conceptualization, Methodology, Writing – original draft, Data curation, Software
Hong, J.Conceptualization, Methodology, Supervision, Writing – review and editing
Mcarthur, D.Conceptualization, Supervision, Writing – review and editing
Livingston, M.Writing – review and editing
Authors: Raturi, V., Hong, J., McArthur, D., and Livingston, M.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Journal of Transport Geography
Publisher:Elsevier
ISSN:0966-6923
ISSN (Online):0966-6923
Published Online:25 March 2021
Copyright Holders:Copyright © 2021 Elsevier Ltd
First Published:First published in Journal of Transport Geography 92:103020
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data
304042UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration